Thoughts, Tips & Insights
Practical AI automation examples that reduce manual work and help coaching businesses scale faster.
Most RAG projects start with a folder of documents, not a user problem. Before you build an index, answer four questions: is the answer in your documents, does the text fit in the prompt, who may see what, and is finding the source really the hard part. Read more
Irresistible Bot took one month and $5,000. That budget doesn't buy an AI strategy. It buys one job done well. Here's the four-part test for which jobs fit in 30 days, and what we'd tell you not to build at that price. Read more
Picking one tool for a whole workflow is the mistake. Split it into steps first. Rules go in code, plumbing between systems goes in a workflow tool like n8n, and a model handles only the steps that are genuinely ambiguous. Here's how to tell which is which. Read more
Coaching platforms are being told to add an AI coach. We'd build the opposite first: automate the admin around each session and leave the coaching to the coach. Here's the split we use, from five years of building Coaching Genie with Milana Leshinsky. Read more
When AI copy sounds generic, teams lengthen the prompt. The model doesn't need more adjectives. It needs facts about each customer's offer and audience, plus samples of how they write, collected up front. Here is how to structure that context, and how Irresistible Bot does it. Read more
A demo proves the model can do the job once, for you, on data you picked. Production asks whether it does the job for every customer, using only their data, at a cost you can carry. Here are the six gaps we check before an AI feature leaves beta. Read more
A company gave every engineer unlimited Claude Code and put up a leaderboard for who burned the most tokens. The post is at 1,000 upvotes. The top comments are about gaming the leaderboard. The leaderboard is the product. Read more
We've shipped 30+ production builds with Claude Code as a primary tool in the last six months. Here's what works, what gets people in trouble, the exact prompt patterns we use, and what the monthly bill looks like. Read more
Most RAG implementations we audit in 2026 still ship naive embedding-cosine retrieval. They work for the demo and break for users. Here's the current bar for production RAG, hybrid search, reranking, citation-required prompts, evals that catch regressions. Read more
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